Optimizing urban car-sharing systems based on geospatial big data and machine learning: A spatio-temporal rebalancing perspective

Car-sharing mobility is an emerging sustainable transportation mode, but it poses great challenges to operators and urban traffic management due to the imbalance between supply and demand across time and space. To address the problem, this research proposes a spatio-temporal rebalancing optimization framework for the urban car-sharing system (CSS) based on geospatial big data and machine learning. In the spatial dimension, we construct the urban car-sharing network data set using geospatial big data. The graph deep learning is used to mine the car-sharing space demand patterns for location planning. This data-driven graph neural network approach breaks through the limitations of complex mathematical models in the previous location planning and can cope with large-scale CSS in real time when data is available. In the temporal dimension, we construct a combined optimization model of dynamic relocation and pricing based on the optimized car-sharing station layout. A multi-threaded reinforcement learning algorithm is proposed to solve the optimal relocation and pricing scheme. Dynamic relocation and pricing strategies are obtained by reinforcement learning algorithms based on accumulated historical operational data and real-time market demand, aiming at maximizing profits and optimizing resource utilization. The simulation results show that the combined optimization model of dynamic relocation and pricing provides a more effective solution than the non-combined model. The proposed optimization framework provides systematic decision support for solving urban CSS supply-demand imbalance and yields extensive theoretical and practical implications, especially in urban traffic management.